Publications by authors named "Alessia Rosso"

2 Publications

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Classification and analysis of outcome predictors in non-critically ill COVID-19 patients.

Intern Med J 2021 04 9;51(4):506-514. Epub 2021 Apr 9.

Department of Medicine, University of Udine, Udine, Italy.

Background: Early detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected patients who could develop a severe form of COVID-19 must be considered of great importance to carry out adequate care and optimise the use of limited resources.

Aims: To use several machine learning classification models to analyse a series of non-critically ill COVID-19 patients admitted to a general medicine ward to verify if any clinical variables recorded could predict the clinical outcome.

Methods: We retrospectively analysed non-critically ill patients with COVID-19 admitted to the general ward of the hospital in Pordenone from 1 March 2020 to 30 April 2020. Patients' characteristics were compared based on clinical outcomes. Through several machine learning classification models, some predictors for clinical outcome were detected.

Results: In the considered period, we analysed 176 consecutive patients admitted: 119 (67.6%) were discharged, 35 (19.9%) dead and 22 (12.5%) were transferred to intensive care unit. The most accurate models were a random forest model (M2) and a conditional inference tree model (M5) (accuracy = 0.79; 95% confidence interval 0.64-0.90, for both). For M2, glomerular filtration rate and creatinine were the most accurate predictors for the outcome, followed by age and fraction-inspired oxygen. For M5, serum sodium, body temperature and arterial pressure of oxygen and inspiratory fraction of oxygen ratio were the most reliable predictors.

Conclusions: In non-critically ill COVID-19 patients admitted to a medical ward, glomerular filtration rate, creatinine and serum sodium were promising predictors for the clinical outcome. Some factors not determined by COVID-19, such as age or dementia, influence clinical outcomes.
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April 2021

Influence of Farming Conditions on the Rumen of Red Deer ().

Animals (Basel) 2019 Aug 23;9(9). Epub 2019 Aug 23.

Department of Biodiversity Protection, Institute of Animal Reproduction and Food Research of Polish Academy of Sciences (IARFR PAS), 10-748 Olsztyn, Poland.

The red deer is an intermediate feeder, showing a marked degree of forage selectivity, with seasonal morphological adaptations due to changes in food quality and availability. In captivity, deer have a limited choice of habitat and food, and we hypothesize that this condition affects the rumen environment. Rumen samples were collected from 20 farmed and 11 wild red deer in autumn 2018 in Poland, and analyzed for chemical composition, food residues, microbial population, and rumen papillation. Farmed deer had the highest spp., and total anaerobic bacteria, but lower spp. Moreover, they showed a decrease in Diplodininae protozoa, and the presence of holotrichs that were absent in the wild animals. The rumen digesta of farmed animals had lower dry matter and acid detergent fiber than the wild ones. The analysis of food residues underlined the poor variety of the diet for animals in the farm. This apparently affected the papillation of the rumen, with animals of the farm having the shortest papillae of the . Overall, results suggest that red deer kept in farms, with a diet based mainly on grass, tree leaves, and some concentrate supplements, undergo a small modification of the rumen compared to the wild conspecifics.
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August 2019